Name
Abstract | ||
|---|---|---|
Estimating 3D pose of a known object from a given 2D image is an important problem with numerous studies for robotics and augmented reality applications. While the state-of-the-art Perspective-n-Point algorithms perform well in pose estimation, the success hinges on whether feature points can be extracted and matched correctly on targets with rich texture. In this work, we propose a robust direct method for 3D pose estimation with high accuracy that performs well on both textured and textureless planar targets. First, the pose of a planar target with respect to a calibrated camera is approximately estimated by posing it as a template matching problem. Next, the object pose is further refined and disambiguated with a gradient descent search scheme. Extensive experiments on both synthetic and real datasets demonstrate the proposed direct pose estimation algorithm performs favorably against state-of-the-art feature-based approaches in terms of robustness and accuracy under several varying conditions. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/WACV.2016.7477640 | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Keywords | Field | DocType |
template matching,approximate estimation,camera calibration,feature point matching,feature point extraction,planar target,3D pose estimation | Template matching,Computer vision,Gradient descent,Pattern recognition,Computer science,3D pose estimation,Augmented reality,Pose,Feature extraction,Robustness (computer science),Artificial intelligence,Motion estimation | Conference |
ISSN | Citations | PageRank |
2472-6737 | 2 | 0.37 |
References | Authors | |
34 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hung-Yu Tseng | 1 | 81 | 6.56 |
| Po-Chen Wu | 2 | 8 | 2.58 |
| Yang Ming-Hsuan | 3 | 15303 | 620.69 |
| Shao-Yi Chien | 4 | 1603 | 154.48 |